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Fast Block LMS Filter

Compute output, error, and weights using LMS adaptive algorithm

Library

Filtering / Adaptive Filters

dspadpt3

Description

The Fast Block LMS Filter block implements an adaptive least mean-square (LMS) filter, where the adaptation of the filter weights occurs once for every
block of data samples. The block estimates the filter weights, or coefficients, needed
to convert the input signal into the desired signal. Connect the signal you want to
filter to the Input port. The input signal can be a scalar or a column vector. Connect
the signal you want to model to the Desired port. The desired signal must have the same
data type, complexity, and dimensions as the input signal. The Output port outputs the
filtered input signal. The Error port outputs the result of subtracting the output
signal from the desired signal.

The block calculates the filter weights using the Block LMS Filter equations. For more
information, see Block LMS Filter.
The Fast Block LMS Filter block implements the convolution operation involved in the
calculations of the filtered output, y, and the weight update
function in the frequency domain using the FFT algorithm used in the Overlap-Save FFT
Filter block. See Overlap-Save FFT Filter (Obsolete) for more information.

Use the Filter length parameter to specify the length of the
filter weights vector.

The Block size parameter determines how many samples of the input
signal are acquired before the filter weights are updated. The input frame length must
be a multiple of the Block size parameter.

The Step-size (mu) parameter corresponds to µ in the equations.
You can either specify a step-size using the input port, Step-size, or enter a value in
the Block Parameters: Block LMS Filter dialog box.

Use the Leakage factor (0 to 1) parameter to specify the leakage
factor, 0<1−μα≤1, in the leaky LMS algorithm shown below.

w(k)=(1−μα)w(k−1)−f(u(n),e(n),μ)

Enter the initial filter weights, w(0), as a vector or a scalar in the Initial value of filter
weights text box. When you enter a scalar, the block uses the scalar
value to create a vector of filter weights. This vector has length equal to the filter
length and all of its values are equal to the scalar value.

When you select the Adapt port check box, an Adapt port appears
on the block. When the input to this port is nonzero, the block continuously updates the
filter weights. When the input to this port is zero, the filter weights remain at their
current values.

When you want to reset the value of the filter weights to their initial values, use
the Reset input parameter. The block resets the filter weights
whenever a reset event is detected at the Reset port. The reset signal rate must be the
same rate as the data signal input.

From the Reset input list, select
None to disable the Reset port. To enable the Reset port,
select one of the following from the Reset input list:

Rising edge — Triggers a reset operation
when the Reset input does one of the following:

Rises from a negative value to a positive value or zero

Rises from zero to a positive value, where the rise is not a
continuation of a rise from a negative value to zero (see the following
figure)

Falling edge — Triggers a reset operation
when the Reset input does one of the following:

Falls from a positive value to a negative value or zero

Falls from zero to a negative value, where the fall is not a
continuation of a fall from a positive value to zero (see the following
figure)

Either edge — Triggers a reset operation
when the Reset input is a Rising edge or
Falling edge (as described above)

Non-zero sample — Triggers a reset operation
at each sample time that the Reset input is not zero

Select the Output filter weights check box to create a Wts port
on the block. For each iteration, the block outputs the current updated filter weights
from this port.

Parameters

Filter length

Enter the length of the FIR filter weights vector. The sum of the
Block size and the Filter
length must be a power of 2.

Block size

Enter the number of samples to acquire before the filter weights are
updated. The number of rows in the input must be an integer multiple of the
Block size. The sum of the Block
size and the Filter length must be a
power of 2.

Specify step-size
via

Select Dialog to enter a value for mu, or
select Input port to specify mu using the
Step-size input port.